##############################################################
# Analisis de Componentes Principales del dataset de Ozono   #
#                                                            #
# Creado: 2020-09-06   v. 2020-09-10                         #
# Ultima Mod: plots                                          #
#                                                            #
# Grupo B: GAD, Benitez, Garcia, Rechimon, Rodriguez         #
#                                                            #
##############################################################

##### IMPORTAMOS LAS BIBLIOTECAS A USAR
library(readxl)
library(corrplot)
## corrplot 0.84 loaded
library(PerformanceAnalytics)
## Loading required package: xts
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## 
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
## 
##     legend
library(psych)
library(rela)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:xts':
## 
##     first, last
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
options(scipen = 6)



###### LEEMOS EL DATASET
data <- read_excel("ozono.xls", na = "?")

# Corroboramos que se haya leido bien
head(data, 5)
## # A tibble: 5 x 74
##   Date                 WSR0  WSR1  WSR2  WSR3  WSR4  WSR5  WSR6  WSR7  WSR8
##   <dttm>              <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1998-01-01 00:00:00   0.8   1.8   2.4   2.1   2     2.1   1.5   1.7   1.9
## 2 1998-01-02 00:00:00   2.8   3.2   3.3   2.7   3.3   3.2   2.9   2.8   3.1
## 3 1998-01-03 00:00:00   2.9   2.8   2.6   2.1   2.2   2.5   2.5   2.7   2.2
## 4 1998-01-04 00:00:00   4.7   3.8   3.7   3.8   2.9   3.1   2.8   2.5   2.4
## 5 1998-01-05 00:00:00   2.6   2.1   1.6   1.4   0.9   1.5   1.2   1.4   1.3
## # ... with 64 more variables: WSR9 <dbl>, WSR10 <dbl>, WSR11 <dbl>,
## #   WSR12 <dbl>, WSR13 <dbl>, WSR14 <dbl>, WSR15 <dbl>, WSR16 <dbl>,
## #   WSR17 <dbl>, WSR18 <dbl>, WSR19 <dbl>, WSR20 <dbl>, WSR21 <dbl>,
## #   WSR22 <dbl>, WSR23 <dbl>, WSR_PK <dbl>, WSR_AV <dbl>, T0 <dbl>, T1 <dbl>,
## #   T2 <dbl>, T3 <dbl>, T4 <dbl>, T5 <dbl>, T6 <dbl>, T7 <dbl>, T8 <dbl>,
## #   T9 <dbl>, T10 <dbl>, T11 <dbl>, T12 <dbl>, T13 <dbl>, T14 <dbl>, T15 <dbl>,
## #   T16 <dbl>, T17 <dbl>, T18 <dbl>, T19 <dbl>, T20 <dbl>, T21 <dbl>,
## #   T22 <dbl>, T23 <dbl>, T_PK <dbl>, T_AV <dbl>, T85 <dbl>, RH85 <dbl>,
## #   U85 <dbl>, V85 <dbl>, HT85 <dbl>, T70 <dbl>, RH70 <dbl>, U70 <dbl>,
## #   V70 <dbl>, HT70 <dbl>, T50 <dbl>, RH50 <dbl>, U50 <dbl>, V50 <dbl>,
## #   HT50 <dbl>, KI <dbl>, TT <dbl>, SLP <dbl>, SLP_ <dbl>, Precp <dbl>,
## #   clase <dbl>
# Visualizamos el numero de variables y de casos
ncol(data)
## [1] 74
nrow(data)
## [1] 2534
##### PREPARACION DE LOS DATOS
# Visualizamos el numero de casos con null
sapply(data, function(x) sum(is.na(x)))
##   Date   WSR0   WSR1   WSR2   WSR3   WSR4   WSR5   WSR6   WSR7   WSR8   WSR9 
##      0    299    292    294    292    293    292    291    289    290    287 
##  WSR10  WSR11  WSR12  WSR13  WSR14  WSR15  WSR16  WSR17  WSR18  WSR19  WSR20 
##    288    292    287    288    288    286    284    283    286    292    294 
##  WSR21  WSR22  WSR23 WSR_PK WSR_AV     T0     T1     T2     T3     T4     T5 
##    293    300    297    273    273    190    185    187    184    184    183 
##     T6     T7     T8     T9    T10    T11    T12    T13    T14    T15    T16 
##    183    183    185    185    188    192    189    191    192    187    184 
##    T17    T18    T19    T20    T21    T22    T23   T_PK   T_AV    T85   RH85 
##    182    184    188    189    185    192    189    175    175     99    105 
##    U85    V85   HT85    T70   RH70    U70    V70   HT70    T50   RH50    U50 
##    180    180     95    107    115    157    157    100    115    125    210 
##    V50   HT50     KI     TT    SLP   SLP_  Precp  clase 
##    210    112    136    125     95    158      2      0
data <- na.omit(data)

sapply(data, function(x) sum(is.na(x)))
##   Date   WSR0   WSR1   WSR2   WSR3   WSR4   WSR5   WSR6   WSR7   WSR8   WSR9 
##      0      0      0      0      0      0      0      0      0      0      0 
##  WSR10  WSR11  WSR12  WSR13  WSR14  WSR15  WSR16  WSR17  WSR18  WSR19  WSR20 
##      0      0      0      0      0      0      0      0      0      0      0 
##  WSR21  WSR22  WSR23 WSR_PK WSR_AV     T0     T1     T2     T3     T4     T5 
##      0      0      0      0      0      0      0      0      0      0      0 
##     T6     T7     T8     T9    T10    T11    T12    T13    T14    T15    T16 
##      0      0      0      0      0      0      0      0      0      0      0 
##    T17    T18    T19    T20    T21    T22    T23   T_PK   T_AV    T85   RH85 
##      0      0      0      0      0      0      0      0      0      0      0 
##    U85    V85   HT85    T70   RH70    U70    V70   HT70    T50   RH50    U50 
##      0      0      0      0      0      0      0      0      0      0      0 
##    V50   HT50     KI     TT    SLP   SLP_  Precp  clase 
##      0      0      0      0      0      0      0      0
nrow(data)
## [1] 1847
# Hacemos un resumen de los datos
summary(data)
##       Date                          WSR0            WSR1           WSR2      
##  Min.   :1998-01-01 00:00:00   Min.   :0.000   Min.   :0.00   Min.   :0.000  
##  1st Qu.:1999-10-14 12:00:00   1st Qu.:0.600   1st Qu.:0.60   1st Qu.:0.600  
##  Median :2001-04-02 00:00:00   Median :1.300   Median :1.30   Median :1.200  
##  Mean   :2001-06-04 17:53:34   Mean   :1.629   Mean   :1.57   Mean   :1.537  
##  3rd Qu.:2003-06-06 12:00:00   3rd Qu.:2.400   3rd Qu.:2.30   3rd Qu.:2.200  
##  Max.   :2004-12-31 00:00:00   Max.   :6.900   Max.   :6.90   Max.   :7.100  
##       WSR3           WSR4            WSR5            WSR6            WSR7      
##  Min.   :0.00   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
##  1st Qu.:0.60   1st Qu.:0.600   1st Qu.:0.700   1st Qu.:0.800   1st Qu.:1.200  
##  Median :1.30   Median :1.300   Median :1.300   Median :1.400   Median :1.900  
##  Mean   :1.52   Mean   :1.515   Mean   :1.534   Mean   :1.632   Mean   :2.038  
##  3rd Qu.:2.20   3rd Qu.:2.200   3rd Qu.:2.100   3rd Qu.:2.200   3rd Qu.:2.800  
##  Max.   :6.70   Max.   :7.200   Max.   :7.400   Max.   :7.200   Max.   :7.500  
##       WSR8            WSR9           WSR10           WSR11          WSR12      
##  Min.   :0.100   Min.   :0.100   Min.   :0.100   Min.   :0.10   Min.   :0.100  
##  1st Qu.:1.700   1st Qu.:2.000   1st Qu.:2.100   1st Qu.:2.00   1st Qu.:2.000  
##  Median :2.400   Median :2.800   Median :2.900   Median :3.00   Median :3.000  
##  Mean   :2.526   Mean   :2.843   Mean   :2.982   Mean   :3.03   Mean   :3.057  
##  3rd Qu.:3.300   3rd Qu.:3.600   3rd Qu.:3.800   3rd Qu.:3.90   3rd Qu.:4.000  
##  Max.   :9.200   Max.   :8.300   Max.   :8.700   Max.   :8.80   Max.   :9.000  
##      WSR13           WSR14           WSR15           WSR16      
##  Min.   :0.100   Min.   :0.100   Min.   :0.000   Min.   :0.200  
##  1st Qu.:2.000   1st Qu.:2.100   1st Qu.:2.200   1st Qu.:2.200  
##  Median :3.000   Median :3.100   Median :3.200   Median :3.200  
##  Mean   :3.114   Mean   :3.185   Mean   :3.235   Mean   :3.202  
##  3rd Qu.:4.100   3rd Qu.:4.200   3rd Qu.:4.200   3rd Qu.:4.150  
##  Max.   :9.600   Max.   :9.100   Max.   :8.900   Max.   :8.700  
##      WSR17           WSR18           WSR19           WSR20      
##  Min.   :0.200   Min.   :0.100   Min.   :0.000   Min.   :0.000  
##  1st Qu.:2.100   1st Qu.:1.700   1st Qu.:1.400   1st Qu.:1.300  
##  Median :2.900   Median :2.500   Median :2.200   Median :2.000  
##  Mean   :2.944   Mean   :2.582   Mean   :2.301   Mean   :2.105  
##  3rd Qu.:3.800   3rd Qu.:3.400   3rd Qu.:3.100   3rd Qu.:2.900  
##  Max.   :8.100   Max.   :7.500   Max.   :7.100   Max.   :8.700  
##      WSR21           WSR22           WSR23           WSR_PK     
##  Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :1.200  
##  1st Qu.:1.100   1st Qu.:0.900   1st Qu.:0.700   1st Qu.:3.400  
##  Median :1.700   Median :1.600   Median :1.400   Median :4.100  
##  Mean   :1.953   Mean   :1.814   Mean   :1.718   Mean   :4.176  
##  3rd Qu.:2.700   3rd Qu.:2.500   3rd Qu.:2.500   3rd Qu.:4.900  
##  Max.   :9.300   Max.   :7.700   Max.   :8.300   Max.   :9.600  
##      WSR_AV            T0              T1              T2       
##  Min.   :0.500   Min.   :-1.80   Min.   :-2.10   Min.   :-2.60  
##  1st Qu.:1.600   1st Qu.:14.10   1st Qu.:13.80   1st Qu.:13.35  
##  Median :2.200   Median :20.70   Median :20.40   Median :20.20  
##  Mean   :2.316   Mean   :18.98   Mean   :18.66   Mean   :18.38  
##  3rd Qu.:2.900   3rd Qu.:24.75   3rd Qu.:24.50   3rd Qu.:24.25  
##  Max.   :6.400   Max.   :29.90   Max.   :29.00   Max.   :28.80  
##        T3              T4              T5              T6       
##  Min.   :-2.10   Min.   :-2.70   Min.   :-2.30   Min.   :-2.20  
##  1st Qu.:13.20   1st Qu.:12.80   1st Qu.:12.60   1st Qu.:12.50  
##  Median :20.20   Median :19.90   Median :19.90   Median :20.00  
##  Mean   :18.14   Mean   :17.92   Mean   :17.79   Mean   :17.92  
##  3rd Qu.:24.10   3rd Qu.:23.90   3rd Qu.:23.90   3rd Qu.:24.20  
##  Max.   :28.30   Max.   :28.10   Max.   :28.20   Max.   :28.70  
##        T7              T8              T9             T10       
##  Min.   :-2.30   Min.   :-1.90   Min.   :-1.20   Min.   :-1.20  
##  1st Qu.:13.20   1st Qu.:14.70   1st Qu.:16.50   1st Qu.:17.90  
##  Median :20.70   Median :21.80   Median :23.20   Median :24.30  
##  Mean   :18.77   Mean   :20.15   Mean   :21.59   Mean   :22.82  
##  3rd Qu.:25.40   3rd Qu.:26.75   3rd Qu.:28.10   3rd Qu.:29.10  
##  Max.   :30.10   Max.   :31.40   Max.   :33.80   Max.   :36.40  
##       T11             T12             T13             T14       
##  Min.   : 0.20   Min.   : 0.30   Min.   : 0.90   Min.   : 1.50  
##  1st Qu.:18.90   1st Qu.:19.80   1st Qu.:20.30   1st Qu.:20.80  
##  Median :25.10   Median :25.60   Median :25.80   Median :25.90  
##  Mean   :23.74   Mean   :24.37   Mean   :24.78   Mean   :25.04  
##  3rd Qu.:29.80   3rd Qu.:30.20   3rd Qu.:30.30   3rd Qu.:30.40  
##  Max.   :38.50   Max.   :40.40   Max.   :41.30   Max.   :41.60  
##       T15             T16             T17             T18            T19       
##  Min.   : 1.70   Min.   : 0.60   Min.   :-0.60   Min.   :-0.2   Min.   : 0.10  
##  1st Qu.:20.90   1st Qu.:20.40   1st Qu.:19.75   1st Qu.:18.6   1st Qu.:17.55  
##  Median :25.80   Median :25.40   Median :24.70   Median :23.8   Median :22.80  
##  Mean   :25.04   Mean   :24.71   Mean   :23.94   Mean   :22.8   Mean   :21.73  
##  3rd Qu.:30.30   3rd Qu.:30.00   3rd Qu.:29.20   3rd Qu.:28.1   3rd Qu.:27.05  
##  Max.   :41.30   Max.   :41.10   Max.   :39.90   Max.   :37.8   Max.   :36.10  
##       T20             T21             T22             T23            T_PK      
##  Min.   : 0.20   Min.   : 0.40   Min.   :-0.10   Min.   :-0.4   Min.   : 1.70  
##  1st Qu.:16.60   1st Qu.:15.70   1st Qu.:15.00   1st Qu.:14.6   1st Qu.:21.70  
##  Median :22.20   Median :21.70   Median :21.30   Median :21.1   Median :26.70  
##  Mean   :20.94   Mean   :20.35   Mean   :19.85   Mean   :19.4   Mean   :25.89  
##  3rd Qu.:26.30   3rd Qu.:25.80   3rd Qu.:25.40   3rd Qu.:25.1   3rd Qu.:31.10  
##  Max.   :34.60   Max.   :33.40   Max.   :32.60   Max.   :31.3   Max.   :41.60  
##       T_AV            T85             RH85             U85         
##  Min.   : 0.30   Min.   :-4.50   Min.   :0.0100   Min.   :-15.770  
##  1st Qu.:16.50   1st Qu.:10.80   1st Qu.:0.3900   1st Qu.: -1.070  
##  Median :22.50   Median :14.40   Median :0.6300   Median :  1.750  
##  Mean   :21.16   Mean   :13.71   Mean   :0.5764   Mean   :  1.976  
##  3rd Qu.:26.80   3rd Qu.:17.40   3rd Qu.:0.7900   3rd Qu.:  4.740  
##  Max.   :33.60   Max.   :24.50   Max.   :1.0000   Max.   : 18.320  
##       V85               HT85           T70              RH70       
##  Min.   :-16.130   Min.   :1357   Min.   :-9.900   Min.   :0.0100  
##  1st Qu.: -2.170   1st Qu.:1514   1st Qu.: 3.700   1st Qu.:0.1700  
##  Median :  1.720   Median :1537   Median : 6.800   Median :0.3700  
##  Mean   :  1.941   Mean   :1534   Mean   : 6.074   Mean   :0.3989  
##  3rd Qu.:  6.080   3rd Qu.:1558   3rd Qu.: 9.000   3rd Qu.:0.6100  
##  Max.   : 22.160   Max.   :1642   Max.   :16.200   Max.   :1.0000  
##       U70               V70               HT70           T50       
##  Min.   :-14.370   Min.   :-23.680   Min.   :2919   Min.   :-24.8  
##  1st Qu.:  0.590   1st Qu.: -2.775   1st Qu.:3121   1st Qu.:-13.2  
##  Median :  4.770   Median :  0.880   Median :3156   Median :-10.1  
##  Mean   :  5.165   Mean   :  1.010   Mean   :3148   Mean   :-10.5  
##  3rd Qu.:  9.790   3rd Qu.:  4.720   3rd Qu.:3182   3rd Qu.: -7.4  
##  Max.   : 28.210   Max.   : 25.540   Max.   :3249   Max.   : -1.7  
##       RH50             U50               V50                HT50     
##  Min.   :0.0100   Min.   :-14.920   Min.   :-25.9900   Min.   :5480  
##  1st Qu.:0.0900   1st Qu.:  2.705   1st Qu.: -3.9900   1st Qu.:5775  
##  Median :0.2200   Median :  9.220   Median :  0.2600   Median :5835  
##  Mean   :0.3001   Mean   :  9.821   Mean   :  0.6472   Mean   :5822  
##  3rd Qu.:0.4700   3rd Qu.: 16.505   3rd Qu.:  4.6450   3rd Qu.:5880  
##  Max.   :1.0000   Max.   : 41.360   Max.   : 30.4200   Max.   :5965  
##        KI               TT              SLP             SLP_          
##  Min.   :-56.70   Min.   :-10.10   Min.   : 9995   Min.   :-135.0000  
##  1st Qu.: -2.75   1st Qu.: 33.05   1st Qu.:10130   1st Qu.: -20.0000  
##  Median : 14.70   Median : 41.35   Median :10160   Median :   0.0000  
##  Mean   : 10.68   Mean   : 37.69   Mean   :10165   Mean   :  -0.8365  
##  3rd Qu.: 27.82   3rd Qu.: 45.15   3rd Qu.:10195   3rd Qu.:  15.0000  
##  Max.   : 42.05   Max.   : 59.15   Max.   :10350   Max.   : 140.0000  
##      Precp             clase       
##  Min.   : 0.0000   Min.   :0.0000  
##  1st Qu.: 0.0000   1st Qu.:0.0000  
##  Median : 0.0000   Median :0.0000  
##  Mean   : 0.3588   Mean   :0.0693  
##  3rd Qu.: 0.0500   3rd Qu.:0.0000  
##  Max.   :20.6500   Max.   :1.0000
# Matriz de correlaciones
cor_data <- cor(data[, -1]) 
corrplot(cor_data) 

#chart.Correlation(data[, -1]) # para graficar correlciones y histogramas



###
# Test de barlett
cortest.bartlett(cor_data, n= 1847)
## $chisq
## [1] 386094.2
## 
## $p.value
## [1] 0
## 
## $df
## [1] 2628
# Test de KMO
KMO(data[, -1])
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data[, -1])
## Overall MSA =  0.91
## MSA for each item = 
##   WSR0   WSR1   WSR2   WSR3   WSR4   WSR5   WSR6   WSR7   WSR8   WSR9  WSR10 
##   0.80   0.86   0.87   0.88   0.88   0.86   0.88   0.79   0.79   0.84   0.82 
##  WSR11  WSR12  WSR13  WSR14  WSR15  WSR16  WSR17  WSR18  WSR19  WSR20  WSR21 
##   0.84   0.84   0.82   0.81   0.81   0.79   0.77   0.77   0.79   0.79   0.81 
##  WSR22  WSR23 WSR_PK WSR_AV     T0     T1     T2     T3     T4     T5     T6 
##   0.85   0.72   0.98   0.70   0.94   0.96   0.95   0.96   0.95   0.95   0.95 
##     T7     T8     T9    T10    T11    T12    T13    T14    T15    T16    T17 
##   0.95   0.95   0.95   0.95   0.95   0.95   0.94   0.95   0.94   0.95   0.95 
##    T18    T19    T20    T21    T22    T23   T_PK   T_AV    T85   RH85    U85 
##   0.95   0.96   0.96   0.96   0.96   0.94   0.99   0.87   0.95   0.80   0.92 
##    V85   HT85    T70   RH70    U70    V70   HT70    T50   RH50    U50    V50 
##   0.93   0.79   0.94   0.70   0.94   0.87   0.92   0.93   0.89   0.97   0.90 
##   HT50     KI     TT    SLP   SLP_  Precp  clase 
##   0.95   0.89   0.83   0.94   0.92   0.83   0.98
# Grafico para ver con cuantas CP nos quedamos
scree(data[, -1])
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.

# Componentes Principales
cp <- prcomp(data[,-1], scale = TRUE)

#Resumen de los Componentes Principales
summary(cp)
## Importance of components:
##                           PC1    PC2     PC3     PC4     PC5     PC6     PC7
## Standard deviation     5.4988 3.9020 2.34036 1.78038 1.61952 1.39171 1.29335
## Proportion of Variance 0.4142 0.2086 0.07503 0.04342 0.03593 0.02653 0.02291
## Cumulative Proportion  0.4142 0.6228 0.69780 0.74123 0.77715 0.80369 0.82660
##                            PC8     PC9    PC10    PC11   PC12    PC13   PC14
## Standard deviation     1.12111 1.03094 0.97706 0.93474 0.9124 0.88738 0.8195
## Proportion of Variance 0.01722 0.01456 0.01308 0.01197 0.0114 0.01079 0.0092
## Cumulative Proportion  0.84382 0.85838 0.87146 0.88342 0.8948 0.90562 0.9148
##                           PC15    PC16   PC17    PC18    PC19    PC20    PC21
## Standard deviation     0.79731 0.74319 0.7248 0.65178 0.60062 0.56083 0.53042
## Proportion of Variance 0.00871 0.00757 0.0072 0.00582 0.00494 0.00431 0.00385
## Cumulative Proportion  0.92352 0.93109 0.9383 0.94411 0.94905 0.95336 0.95721
##                           PC22    PC23    PC24    PC25    PC26    PC27    PC28
## Standard deviation     0.51635 0.48805 0.47955 0.45328 0.42993 0.41354 0.39410
## Proportion of Variance 0.00365 0.00326 0.00315 0.00281 0.00253 0.00234 0.00213
## Cumulative Proportion  0.96086 0.96413 0.96728 0.97009 0.97262 0.97497 0.97709
##                           PC29    PC30    PC31    PC32    PC33    PC34    PC35
## Standard deviation     0.37897 0.35346 0.33661 0.33322 0.32211 0.30525 0.29897
## Proportion of Variance 0.00197 0.00171 0.00155 0.00152 0.00142 0.00128 0.00122
## Cumulative Proportion  0.97906 0.98077 0.98232 0.98384 0.98527 0.98654 0.98777
##                           PC36   PC37   PC38    PC39    PC40    PC41    PC42
## Standard deviation     0.29204 0.2837 0.2699 0.26409 0.26011 0.24888 0.24464
## Proportion of Variance 0.00117 0.0011 0.0010 0.00096 0.00093 0.00085 0.00082
## Cumulative Proportion  0.98894 0.9900 0.9910 0.99199 0.99292 0.99377 0.99459
##                           PC43   PC44    PC45   PC46    PC47    PC48    PC49
## Standard deviation     0.23653 0.2263 0.21912 0.2096 0.20121 0.17978 0.14170
## Proportion of Variance 0.00077 0.0007 0.00066 0.0006 0.00055 0.00044 0.00028
## Cumulative Proportion  0.99535 0.9960 0.99671 0.9973 0.99787 0.99831 0.99859
##                           PC50    PC51    PC52    PC53    PC54    PC55    PC56
## Standard deviation     0.12678 0.12412 0.11673 0.09661 0.09552 0.08146 0.07355
## Proportion of Variance 0.00022 0.00021 0.00019 0.00013 0.00012 0.00009 0.00007
## Cumulative Proportion  0.99881 0.99902 0.99920 0.99933 0.99946 0.99955 0.99962
##                           PC57    PC58    PC59    PC60    PC61    PC62    PC63
## Standard deviation     0.06466 0.06218 0.05704 0.05084 0.04829 0.04369 0.04249
## Proportion of Variance 0.00006 0.00005 0.00004 0.00004 0.00003 0.00003 0.00002
## Cumulative Proportion  0.99968 0.99973 0.99978 0.99981 0.99984 0.99987 0.99989
##                           PC64    PC65    PC66    PC67    PC68    PC69    PC70
## Standard deviation     0.03750 0.03411 0.03324 0.02997 0.02883 0.02751 0.02460
## Proportion of Variance 0.00002 0.00002 0.00002 0.00001 0.00001 0.00001 0.00001
## Cumulative Proportion  0.99991 0.99993 0.99995 0.99996 0.99997 0.99998 0.99999
##                           PC71    PC72     PC73
## Standard deviation     0.02234 0.01989 0.004221
## Proportion of Variance 0.00001 0.00001 0.000000
## Cumulative Proportion  0.99999 1.00000 1.000000
names(cp)
## [1] "sdev"     "rotation" "center"   "scale"    "x"
cp$center  # Media de cada variable
##           WSR0           WSR1           WSR2           WSR3           WSR4 
##     1.62902003     1.57038441     1.53697888     1.52008663     1.51526800 
##           WSR5           WSR6           WSR7           WSR8           WSR9 
##     1.53367623     1.63243097     2.03789930     2.52577152     2.84315106 
##          WSR10          WSR11          WSR12          WSR13          WSR14 
##     2.98159177     3.02972388     3.05668652     3.11429345     3.18500271 
##          WSR15          WSR16          WSR17          WSR18          WSR19 
##     3.23459664     3.20184082     2.94385490     2.58175420     2.30135355 
##          WSR20          WSR21          WSR22          WSR23         WSR_PK 
##     2.10481862     1.95343801     1.81402274     1.71754196     4.17623173 
##         WSR_AV             T0             T1             T2             T3 
##     2.31613427    18.98186248    18.66134272    18.38213319    18.13508392 
##             T4             T5             T6             T7             T8 
##    17.92008663    17.78673525    17.92403898    18.77249594    20.14775311 
##             T9            T10            T11            T12            T13 
##    21.58570655    22.82149432    23.73936113    24.36924743    24.78413644 
##            T14            T15            T16            T17            T18 
##    25.03789930    25.03654575    24.71402274    23.93795344    22.80498105 
##            T19            T20            T21            T22            T23 
##    21.73470493    20.94087710    20.35473741    19.84661613    19.39794261 
##           T_PK           T_AV            T85           RH85            U85 
##    25.88787223    21.15944775    13.71396860     0.57639957     1.97608013 
##            V85           HT85            T70           RH70            U70 
##     1.94066594  1533.69978343     6.07374120     0.39885219     5.16504061 
##            V70           HT70            T50           RH50            U50 
##     1.01033568  3148.36410395   -10.50070384     0.30008663     9.82119112 
##            V50           HT50             KI             TT            SLP 
##     0.64720087  5822.42555495    10.68040065    37.68933406 10165.47644829 
##           SLP_          Precp          clase 
##    -0.83649161     0.35878722     0.06930157
cp$scale   #  Desviacion estandard de cada variable
##       WSR0       WSR1       WSR2       WSR3       WSR4       WSR5       WSR6 
##  1.2532611  1.2437166  1.2188223  1.1948800  1.1875413  1.1587215  1.1376230 
##       WSR7       WSR8       WSR9      WSR10      WSR11      WSR12      WSR13 
##  1.1543284  1.1723598  1.2082577  1.2908659  1.3787935  1.4098352  1.4312642 
##      WSR14      WSR15      WSR16      WSR17      WSR18      WSR19      WSR20 
##  1.4196719  1.3722366  1.2737354  1.2360134  1.2425499  1.2302099  1.2227545 
##      WSR21      WSR22      WSR23     WSR_PK     WSR_AV         T0         T1 
##  1.2268828  1.2465134  1.2809022  1.1738729  0.9194124  6.7906970  6.8566936 
##         T2         T3         T4         T5         T6         T7         T8 
##  6.9188639  6.9765000  7.0231379  7.0843190  7.2866748  7.6174112  7.5985732 
##         T9        T10        T11        T12        T13        T14        T15 
##  7.4621944  7.3728351  7.3057891  7.2508539  7.1722525  7.0984261  7.0335815 
##        T16        T17        T18        T19        T20        T21        T22 
##  6.9870445  6.9459143  6.8523238  6.6863457  6.6180563  6.6115504  6.6437475 
##        T23       T_PK       T_AV        T85       RH85        U85        V85 
##  6.6997878  6.8585771  6.7485889  4.7600549  0.2546395  4.5163844  6.0906795 
##       HT85        T70       RH70        U70        V70       HT70        T50 
## 35.3220501  3.7995667  0.2619168  6.3273848  6.2918610 46.6540766  3.8053869 
##       RH50        U50        V50       HT50         KI         TT        SLP 
##  0.2445791  9.3428061  7.3520233 75.7110874 20.1705541 11.0074477 52.0564674 
##       SLP_      Precp      clase 
## 34.1348151  1.2625728  0.2540350
cp$sdev    # Varianza de cada componente
##  [1] 5.498767773 3.902047771 2.340364190 1.780378929 1.619516746 1.391706901
##  [7] 1.293346613 1.121108044 1.030940921 0.977055803 0.934741210 0.912429743
## [13] 0.887376899 0.819502283 0.797312911 0.743189056 0.724785586 0.651782020
## [19] 0.600615142 0.560833724 0.530420651 0.516352374 0.488048006 0.479551788
## [25] 0.453278141 0.429932314 0.413538248 0.394103454 0.378974051 0.353456841
## [31] 0.336606710 0.333215605 0.322114566 0.305253491 0.298967201 0.292041287
## [37] 0.283660896 0.269932364 0.264089940 0.260109298 0.248875711 0.244641934
## [43] 0.236525329 0.226346963 0.219122578 0.209591385 0.201214080 0.179778346
## [49] 0.141698627 0.126783554 0.124121998 0.116729060 0.096606035 0.095521667
## [55] 0.081456190 0.073554445 0.064660029 0.062178873 0.057043689 0.050840701
## [61] 0.048291880 0.043685167 0.042487348 0.037497870 0.034107788 0.033242903
## [67] 0.029965233 0.028833463 0.027505974 0.024602185 0.022337151 0.019891745
## [73] 0.004220875
#cp$rotation 
#cp$x


# para graficar:
biplot(x = cp, scale = 0, cex = 0.6, col = c("grey", "brown3"))

pc1 <- cp[[2]][,1]
pc2 <- cp[[2]][,2]
pc3 <- cp[[2]][,3]
pc4 <- cp[[2]][,4]
pc5 <- cp[[2]][,5]
pc6 <- cp[[2]][,6]
pc7 <- cp[[2]][,7]

pc <- data.frame(cbind(pc1, pc2, pc3, pc4, pc5, pc6, pc7))

rm(pc1, pc2, pc3, pc4, pc5, pc6, pc7)

fig <- plot_ly(x = ~pc[1,], y = ~pc$pc1, name = "PC1", type = "bar", text = rownames(pc), textangle=-90,  textposition='auto')%>% 
  layout(
    title = "PC1",
    xaxis = list(title = "Variables", showticklabels=FALSE),
    yaxis = list(title = "Valores")
  )
fig
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
fig1 <- plot_ly(x = ~pc[2,], y = ~pc$pc2, name = "PC2", type = "bar", text = rownames(pc), textangle=-90,  textposition='auto')%>% 
  layout(
    title = "PC2",
    xaxis = list(title = "Variables", showticklabels=FALSE),
    yaxis = list(title = "Valores")
  )
fig1
fig2 <- plot_ly(x = ~pc[2,], y = ~pc$pc3, name = "PC3", type = "bar", text = rownames(pc), textangle=-90,  textposition='auto')%>% 
  layout(
    title = "PC3",
    xaxis = list(title = "Variables", showticklabels=FALSE),
    yaxis = list(title = "Valores")
  )
fig2